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  • Title: Estimation and prediction of renal function in patients with renal tumor.
    Author: Kim HL, Shah SK, Tan W, Shikanov SA, Zorn KC, Shalhav AL, Wilding GE.
    Journal: J Urol; 2009 Jun; 181(6):2451-60; discussion 2460-1. PubMed ID: 19371883.
    Abstract:
    PURPOSE: The goals of surgery for renal tumors include the preservation of renal function. When considering surgical options, it is important to accurately assess renal function and the risk of postoperative chronic kidney disease. MATERIALS AND METHODS: An institutional database was used to identify 359 patients who underwent nephrectomy or partial nephrectomy. Creatinine clearance was estimated using 14 previously published models and compared with creatinine clearance measured using a 24-hour urine collection. Models were generated for predicting renal function following nephrectomy or partial nephrectomy. All models were validated with an external data set of 245 patients. RESULTS: Models that accurately estimated creatinine clearance preoperatively and postoperatively were the Cockcroft-Gault model based on actual weight, and the Mawer, Björnsson, Hull and Martin models. In patients with an estimated creatinine clearance between 60 and 89 ml per minute preoperatively the risk of chronic kidney disease (creatinine clearance less than 60 ml per minute) after nephrectomy and partial nephrectomy was 58% and 15%, respectively (p <0.001). In patients undergoing nephrectomy age and weight were independent predictors of decreased creatinine clearance. A predictive model based on age and weight was highly accurate when applied to an external population (R = 0.757). A model for predicting renal function after partial nephrectomy based on age and tumor size was highly accurate in the external population (R = 0.848). A Web based tool was developed to estimate current and predict postoperative creatinine clearance (http://www.roswellpark.org/Patient_Care/Specialized_Services/Renal_Function_Estimator). CONCLUSIONS: The Cockcroft-Gault model based on actual weight is 1 of 5 models that accurately estimates renal function in patients with a kidney tumor. Models were developed and externally validated to predict renal function following nephrectomy.
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